Towards clustering of incomplete microarray data without the use of imputation

Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessingstep,theyarenotchangedduringsubsequentprocesses of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results.Thus, a newclustering methodis requiredfor improving missing values during iterative clustering process. Results: We present a method for Clustering Incomplete data using Alte...

• Type: Journal
• Year: 2007
• Where: BIOINFORMATICS
• Authors: Dae-Won Kim, Ki Young Lee, Kwang H. Lee, Doheon Lee
• Keywords: BIOINFORMATICS 2007, Clustering Methods, Iterative Clustering Process, Missing Values